Objective To establish a DNA quantitation method based on dithiothreitol(DTT)-crystal vio-let.Methods DTT was used to decolorize crystal violet,mixed with different concentrations of λ-DNA and salmon sperm DNA standard samples or concentration standard samples,and the absorbance was read at 595 nm wavelength by microplate reader,and compared with the results of ultraviolet absorbance method.DTT-crystal violet method and ultraviolet absorbance method were used to compare the concentration of plasmid samples and the concentration of genomic DNA samples of cervical exfoliated cells.The protein tolerance of the two methods was assessed by simulating protein contaminants with bovine serum albumin(BSA).Results In the quantification of λ-DNA and salmon sperm DNA,the DTT-crystal violet method had a robust linear correlation between the absorbance at 595 nm and DNA concentration(r2＞0.95),and the measured concentrations of the standard samples were not significantly different from the theoretical concentrations of the prepared standard samples(P＞0.05).There was no significant difference in the concentration of plasmid samples measured by DTT-crystal violet method and ultraviolet absorption method(P＞0.05).The concen-tration of DNA samples from cervical exfoliated cells measured by ultraviolet absorption method was positive-ly correlated with that by DTT-crystal violet method(r=0.94,P＜0.01).The concentration of the standard sample containing BSA 1 μg/μL measured by ultraviolet absorption method was higher than that of the con-trol sample,and the difference was statistically significant(P＜0.01),whereas the DTT-crystal violet method was not significantly affected(P＞0.05).Conclusion DTT-crystal violet method has obvious advantages over the existing DNA quantitation method,and is suitable for DNA quantitative analysis in scientific research and clinic.

Alzheimer's disease (AD) is a chronic neurodegenerative disease that mainly affects neurons in the brain,especially in regions related to memory,thinking,and behavior. During the auxiliary diagnosis of AD,massive data from imaging,genetics,transcriptomics as well as clinical features provide a new basis for mining potential molecular markers and the early diagnosis and intervention of AD. In recent years,deep learning models have shown strong feature learning and prediction capabilities in AD image classification;and the researchers will effectively integrate various modal data to provide richer complementary information for further improving the classification performance. Herein the review introduces the commonly used neuroimaging data sets and evaluation criteria for AD,analyzes the application of various modal data in AD classification,focusing on the application of multimodal data in AD classification diagnosis,discuss the application of the classic deep learning network model in AD classification diagnosis,aiming to provide ideas for further research on multimodal deep learning technology.

Alzheimer's disease (AD) is a chronic neurodegenerative disease that mainly affects neurons in the brain,especially in regions related to memory,thinking,and behavior. During the auxiliary diagnosis of AD,massive data from imaging,genetics,transcriptomics as well as clinical features provide a new basis for mining potential molecular markers and the early diagnosis and intervention of AD. In recent years,deep learning models have shown strong feature learning and prediction capabilities in AD image classification;and the researchers will effectively integrate various modal data to provide richer complementary information for further improving the classification performance. Herein the review introduces the commonly used neuroimaging data sets and evaluation criteria for AD,analyzes the application of various modal data in AD classification,focusing on the application of multimodal data in AD classification diagnosis,discuss the application of the classic deep learning network model in AD classification diagnosis,aiming to provide ideas for further research on multimodal deep learning technology.

Gas therapy is emerging as a highly promising therapeutic strategy for cancer treatment. However, there are limitations, including the lack of targeted subcellular organelle accuracy and spatiotemporal release precision, associated with gas therapy. In this study, we developed a series of photoactivatable nitric oxide (NO) donors NRh-R-NO (R = Me, Et, Bn, iPr, and Ph) based on an N-nitrosated upconversion luminescent rhodamine scaffold. Under the irradiation of 808 nm light, only NRh-Ph-NO could effectively release NO and NRh-Ph with a significant turn-on frequency upconversion luminescence (FUCL) signal at 740 nm, ascribed to lower N-N bond dissociation energy. We also investigated the involved multistage near-infrared-controlled cascade release of gas therapy, including the NO released from NRh-Ph-NO along with one NRh-Ph molecule generation, the superoxide anion O₂^⋅- produced by the photodynamic therapy (PDT) effect of NRh-Ph, and highly toxic peroxynitrite anion (ONOO^‒) generated from the co-existence of NO and O₂^⋅-. After mild nano-modification, the nanogenerator (NRh-Ph-NO NPs) empowered with superior biocompatibility could target mitochondria. Under an 808 nm laser irradiation, NRh-Ph-NO NPs could induce NO/ROS to generate RNS, causing a decrease in the mitochondrial membrane potential and initiating apoptosis by caspase-3 activation, which further induced tumor immunogenic cell death (ICD). In vivo therapeutic results of NRh-Ph-NO NPs showed augmented RNS-potentiated gas therapy, demonstrating excellent biocompatibility and effective tumor inhibition guided by real-time FUCL imaging. Collectively, this versatile strategy defines the targeted RNS-mediated cancer therapy.

Objective:To investigate the effect of Early infarct growth rate(EIGR) on the prognosis of patients with acute large vessel occlusive ischemic stroke.Methods:A total of 164 patients with acute large vessel occlusive ischemic stroke were enrolled in the emergency department of the First Affiliated Hospital of Nanjing Medical University from January 1, 2020 to December 31, 2022.According to the change of the National Institutes of Health Stroke Scale (NIHSS) score at admission and 72 h after treatment, the patients were divided into good prognosis group and poor prognosis group. The basic clinical data of the two groups were observed and compared. The risk factors of poor prognosis were analyzed by univariate regression. The effect of EIGR on prognosis after age stratification was further analyzed.Results:Comparing the clinical data of the two groups, there was no difference in EIGR (mL/h) (7.67 vs. 8.24, P=0.211) between the two groups. The product between EIGR and age was included as the interaction term, and the result of the interaction term in the model was statistically significant ( OR=1.002, 95% CI: 1.000-1.003, P=0.032) .Moreover, the result was still statistically significant after adjusting for relevant variables (gender, history of hypertension, history of atrial fibrillation, history of diabetes, history of coronary heart disease, and history of stroke) ( OR=1.002, 95% CI:1.000-1.003, P=0.027). Subgroup analysis was performed according to the median age (71 years). In the elderly group, the proportion of poor prognosis was higher with fast core infarction growth rate defined by 25 mL/h and 15 mL/h ( P < 0.05).In the younger age group, there was no significant difference in the proportion of poor prognosis in the fast core infarction growth rate compared with the slow type ( P > 0.05). Conclusions:EIGR can predict the early clinical outcome early in elderly patients with large vessel occlusive ischemic stroke.

With the intensify of aging population,accurate assessment of brain health becomes more and more important.Brain age can reflect brain health and cognitive function.Based on different algorithms,modal data and central datasets,imaging is helpful for predicting brain age.The progresses of imaging researches for predicting brain age were reviewed in this article.

Parkinson's disease(PD)is the second most common neurodegenerative disease after Alzheimer's disease,and the early diagnosis and intervention are crucial for patients.The review focuses on machine learning for intelligent diagnosis of PD.The common machine learning algorithms in PD diagnosis,specifically convolutional neural networks and long short-term memory networks,are introduced,and their applications in medical image analysis and motor behavior analysis are discussed in details.By comparing relevant domestic and international researches,the advantages and disadvantages of using different imaging and kinematic data for PD diagnosis are analyzed.Finally,the review summarizes and presents a prospect for the application of machine learning in PD diagnosis.

Objective:To compare the consistency of lymphoma multigene detection panels based on next-generation sequencing (NGS) with FISH detection of B-cell lymphoma gene rearrangement.Methods:From January 2019 to May 2023, fusion genes detected by lymphoma-related 413 genes that targeted capture sequencing of 489 B-cell lymphoma tissues embedded in paraffin were collected from Henan Cancer Hospital, and the results were compared with simultaneous FISH detection of four break/fusion genes: BCL2, BCL6, MYC, and CCND1. Consistency was defined as both methods yielding positive or negative results for the same sample. The relationship between fusion mutation abundance in NGS and the positivity rate of cells in FISH was also analyzed.Results:Kappa consistency analysis revealed high consistency between NGS and FISH in detecting the four B-cell lymphoma-related gene rearrangement ( P<0.001 for all) ; however, the detection rates of positive individuals differed for the four genes. Compared with FISH, NGS demonstrated a higher detection rate for BCL2 rearrangement, a lower detection rate for BCL6 and MYC rearrangement, and a similar detection rate for CCND1 rearrangement. No correlation was found between fusion mutation abundance in NGS and the positivity rate of cells in FISH. Conclusions:NGS and FISH detection of B-cell lymphoma gene rearrangement demonstrate overall good consistency. NGS is superior to FISH in detecting BCL2 rearrangement, inferior in detecting MYC rearrangement, and comparable in detecting CCND1 rearrangement.

Objective To investigate the prognostic effect of N-terminal domain of gasdermin-D(GSDMD-N)on the short-and long-term outcomes of patients with large-vessel occlusion(LVO)ischemic stroke.Methods A retrospective study was conducted on 65 patients with acute LVO is-chemic stroke admitted to Emergency Medicine Center of the First Affiliated Hospital of Nanjing Medical University from January to April 2023.According to their NIHSS score at and 5 d after admission,they were divided into a good prognosis group 1(34 cases)and a poor prognosis group 1(31 cases),and based on their mRS score at 90 d,they were also assigned into a good prognosis group 2(47 cases)and a poor prognosis group 2(18 cases).Their baseline data were collected,and univariate and multivariate regression analyses were performed to identify potential risk factors affecting short-term and long-term prognosis.GSDMD-N level was measured at and 24 h after ad-mission.ROC curves were plotted to analyze the predictive performance of GSDMD-N for poor prognosis.Results There were no significant differences between the good prognosis group 1 and the poor prognosis group 1 in terms of age,male,perfusion defect volume,time from onset to ad-mission,NIHSS score at admission,levels of GSDMD-N,S100β or syntaxin 17(STX17),hemor-rhagic complications,or ratios of intravenous thrombolysis,endovascular therapy,hypertension,diabetes,coronary heart disease,previous stroke,or atrial fibrillation(P＞0.05).Univariate and multivariate logistic regression analyses revealed that change in GSDMD-N from admission to 24 h later was an important factor influencing short-term poor prognosis(OR=1.054,95％CI:1.023-1.093,P=0.001;OR=1.072,95％CI:1.032-1.124,P=0.001).ROC curve analysis showed that change of GSDMD-N had good predictive value for short-term poor prognosis,with an AUC value of 0.814(95％CI:0.698-0.905).No statistical differences were observed between the good prog-nosis group 2 and poor prognosis group 2 in above indicators as between the good prognosis group 1 and poor prognosis group 1(P＞0.05).Univariate and multivariate logistic regression analyses revealed that change of GSDMD-N from admission to 24 h later was an important influencing fac-tor for long-term poor prognosis(OR=1.001,95％CI:1.023-1.069,P=0.046;OR=1.063,95％CI:1.017-1.125,P=0.015).ROC curve analysis displayed that the change also showed good pre-dictive value for long-term poor prognosis,with an AUC value of 0.881(95％CI:0.767-0.961).Conclusion Change in GSDMD-N from admission to 24 h later can predict the short-and long-term prognosis of patients with LVO ischemic stroke.

Objective To investigate the prognostic effect of N-terminal domain of gasdermin-D(GSDMD-N)on the short-and long-term outcomes of patients with large-vessel occlusion(LVO)ischemic stroke.Methods A retrospective study was conducted on 65 patients with acute LVO is-chemic stroke admitted to Emergency Medicine Center of the First Affiliated Hospital of Nanjing Medical University from January to April 2023.According to their NIHSS score at and 5 d after admission,they were divided into a good prognosis group 1(34 cases)and a poor prognosis group 1(31 cases),and based on their mRS score at 90 d,they were also assigned into a good prognosis group 2(47 cases)and a poor prognosis group 2(18 cases).Their baseline data were collected,and univariate and multivariate regression analyses were performed to identify potential risk factors affecting short-term and long-term prognosis.GSDMD-N level was measured at and 24 h after ad-mission.ROC curves were plotted to analyze the predictive performance of GSDMD-N for poor prognosis.Results There were no significant differences between the good prognosis group 1 and the poor prognosis group 1 in terms of age,male,perfusion defect volume,time from onset to ad-mission,NIHSS score at admission,levels of GSDMD-N,S100β or syntaxin 17(STX17),hemor-rhagic complications,or ratios of intravenous thrombolysis,endovascular therapy,hypertension,diabetes,coronary heart disease,previous stroke,or atrial fibrillation(P＞0.05).Univariate and multivariate logistic regression analyses revealed that change in GSDMD-N from admission to 24 h later was an important factor influencing short-term poor prognosis(OR=1.054,95％CI:1.023-1.093,P=0.001;OR=1.072,95％CI:1.032-1.124,P=0.001).ROC curve analysis showed that change of GSDMD-N had good predictive value for short-term poor prognosis,with an AUC value of 0.814(95％CI:0.698-0.905).No statistical differences were observed between the good prog-nosis group 2 and poor prognosis group 2 in above indicators as between the good prognosis group 1 and poor prognosis group 1(P＞0.05).Univariate and multivariate logistic regression analyses revealed that change of GSDMD-N from admission to 24 h later was an important influencing fac-tor for long-term poor prognosis(OR=1.001,95％CI:1.023-1.069,P=0.046;OR=1.063,95％CI:1.017-1.125,P=0.015).ROC curve analysis displayed that the change also showed good pre-dictive value for long-term poor prognosis,with an AUC value of 0.881(95％CI:0.767-0.961).Conclusion Change in GSDMD-N from admission to 24 h later can predict the short-and long-term prognosis of patients with LVO ischemic stroke.